Quantitative Proteomics and Data Analysis

Laurent Gatto

Inspection, visualisation and analysis of quantitative proteomics data

Laurent Gatto                      Computational Proteomics Unit
https://lgatto.github.io           University of Cambridge

Slides: http://bit.ly/qprotda – Code: http://bit.ly/qprotdar

Acknowledgements BBSRC for funding; Sebastian Gibb and Lisa Breckels for coding.

(Last update Sun Apr 3 22:56:46 2016)

These slides are available under a creative common CC-BY license. You are free to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) for any purpose, even commercially.

Content

  1. Introduction on data analysis
  2. Quantitative proteomics data analysis - overview
  3. Visualisation
  4. Quantitative proteomics data analysis - examples
  5. Data analysis
  6. References and resources

What is data analysis

Data analysis is the process by which data becomes understanding, knowledge and insight. Hadley Wickham

The ability to prepare and explore data, identify patterns (good and pathological ones) and convincingly demonstrate that the pattern are genuine (rather than random).

It’s not analysing data, it’s investigating data - requires flexibility.

Why programme

But:

To analyse data, you need

To analyse data, you need

To analyse data, you need

Quantitative proteomics data analysis

Visualisation

A picture is worth a thousand words.

Graphics reveal data.

Visualization can surprise you, but it doesn’t scale well. Modeling scales well, but it can’t surprise you. Hadley Wickham

Inspection, visualisation and analysis of quantitative proteomics data

Data analysis tools

should enables you to manipulate your data, give some guarantees about the integrity of the data, support effective extract/subset components of the data, visualise them, enable transformation of the data, give access to infrastucture for statistical analysis, and enable annotation of the data.

Data analysis tools

The MSnSet class for quantitative data

Can be subsetted, transformed, visualised, annotated, statistics, …

References, resources

Thank you for your attention